International Research Fellowship Program: Intelligent Agent Optimization of Transit Route Network Design

国际研究奖学金计划使美国科学家和工程师能够在国外进行9到24个月的研究。该计划的奖项提供了联合研究的机会，并利用国外独特或互补的设施、专业知识和实验条件。该奖项将支持Jeremy J.Blum博士与印度孟买印度理工学院Tom Mathew博士和M.C.Deo博士合作的为期12个月的研究奖学金。该项目的目标是改进公交系统的路线和时刻表的设计，这是一个潜在的全球应用问题。这项研究将利用PI的论文研究中的建模和仿真技术，并扩展他在货运铁路流量优化方面的研究。他们与Union Switch和Signal合作，将有效的智能代理优化系统集成到他们的列车调度系统中。这项研究的智力价值在于加深了对智能代理系统在具有多个竞争目标函数的优化问题中的应用的理解。在理论层面上，这项研究将评估系统的结构能力，以管理这个多准则优化问题中的竞争目标。本文的理论研究无论是传播到智能智能体优化系统中，还是将其应用于公交系统的路径优化，都将产生更广泛的研究影响。他们预计，这项研究将证实这样的假设，即包含多算法优化方法的智能代理团队将比目前基于个体启发式或通用优化技术的方法更有效和高效地解决公交路线网络设计(TRND)问题。由于印度的交通系统规模庞大，这是测试这些优化系统的可扩展性的理想场所。该项目的技术方法始于创建一个用于公交优化的智能代理系统。与以前解决这个问题的方法不同，它们将识别广泛的相关领域特定的启发式和通用优化技术，然后将这些算法编码为代理。然后，将智能代理系统的性能与同类方法进行比较，其中包括个体启发式或元启发式。本研究的预期结果是，智能代理系统将产生比同构方法更好的解决方案，并且新的机制将产生更有效的系统。

0700998BlumThe International Research Fellowship Program enables U.S. scientists and engineers to conduct nine to twenty-four months of research abroad. The program's awards provide opportunities for joint research, and the use of unique or complementary facilities, expertise and experimental conditions abroad.This award will support a twelve-month research fellowship by Dr. Jeremy J. Blum to work with Drs. Tom Mathew and Dr. M. C. Deo at the Indian Institute of Technology in Bombay, India.The goal of this project is to improve the design of routes and schedules for bus transit systems, a problem with potential global application. The proposed research will utilize modeling and simulation techniques from the PI's dissertation research, and extend his research in the flow optimization for freight railroads. In collaboration with Union Switch and Signal, they integrated an effective intelligent agent optimization system with their train dispatch system. The intellectual merit of this research is the development of a deeper understanding of the application of intelligent agent systems to optimization problems with multiple, competing objective functions. On a theoretical level, this research will assess the ability of the system to be structured to manage the competing objectives in this multi-criteria optimization problem. Dissemination of both this theoretical research in intelligent agent optimization systems and its application to the bus transit system routing will produce the broader impacts of the research. They expect that this research will confirm the hypothesis that a team of intelligent agents,encompassing a multi-algorithmic approach to optimization, will more effectively and efficiently solve the Transit Route Network Design (TRND) problem than current approaches based on individual heuristics or generic optimization techniques. Due to the large size of transit systems in India, it is an ideal place to test the scalability of these optimization systems. The technical approach for this project begins with the creation of an intelligent agent system for transit optimization. Unlike previous approaches to this problem, they will identify a wide range of relevant domain-specific heuristics and generic optimization techniques and then encode these algorithms as agents. Then, the performance of the intelligent agent system will be compared with homogeneous approaches, consisting of individual heuristics or meta-heuristics. The expected results of this research are that the intelligent agent system will produce better solutions than homogeneous approaches and that the new mechanisms will produce a more efficient system.